Claire Val: What is stacked around your office right now?

Alex Finn: I have three Mac Studio 512 GB. We got a DGX Spark as well as a computer. I just built an RTX 5090. Basically, at all times of the day, each one of these computers is just burning tokens. The number one push back I get on all this is, "Your computers are so expensive. Isn't cloud models cheap? Isn't it $20 for a Chad GBT subscription?" Well, that's not the point. The point isn't pure ROI. The point is the use cases it unlocks. You now have because you have AI models running locally, the ability to run unlimited intelligence around the clock 24/7. If you were to do that with a cloud model like Chad GBT or Claude, you would be spending outrageous amounts of money.

Claire Val: What else fun are you doing with AI?

Alex Finn: The most fun I've been having lately is building out my software factory. I have two loops in cloud code going. I have a build loop and a review loop. First, it has a build loop where it'll take all those tasks and start building out the tasks it came with over and over and over again. And then it has a review loop that takes all the tasks that were built and has another agent go in and review it. Once that's reviewed, it pings me on Slack and I can just leave a rocket emoji and when I leave the rocket emoji, it says merged and my Henry loop goes in and merges it. It's been a blast kind of cracking this nut of how do you build your own software factory.

Claire Val: Welcome back to how I AI. I'm Claire Val, product leader and AI obsessive here on a mission to help you build better with these new tools. Today I have Alex Finn and he's going to walk us through his two Mac Studios, DJX Spark and the Nvidia backed computer he built for himself and demystify what it means to run local models and have ambient AI working for you 24 hours a day. [music] Let's get to it. This episode is brought to you by Runway, a new kind of creative platform that has everything [music] you need to generate any image, video, or piece of content you want, all in one place. With Runway, it's now possible to go from initial idea to a finished deliverable in a matter of minutes. From turning lowfidelity product shots into campaign ready imagery all the way through putting together big brand films, Runway can help your team scale your creative ambitions while keeping your budgets and timelines from doing the same. Runray brings together the world's most advanced AI models, which is why enterprises like Microsoft, Robin Hood, Amazon, and Adobe along with studios like Lionsgate, and Legendary all use Runway to ship real work every day. Try it yourself at runwayml.com/howi. Promo code how I AI. Alex, welcome to How I AI hardware edition, local model edition. I am so excited. So, before you jump in, tell us just what is stacked around your office right now.

Alex Finn: It uh it feels like a sauna in this office right now to be quite honest with you because I I got a lot going. Uh I have three Mac Studio 512 gigabytes which apparently puts me uh in the in the wealth class of Elon Musk having these now. I think they resell for like $30,000 each. Um, we got a DGX Spark uh as well as a computer. I just built uh two days ago a basically a big computer built around an RTX 5090. So we have I think what's that come out to five or six different computers for AI. So a lot a lot of hardware around here

Claire Val: and you are making really good use of it.

Alex Finn: Yeah. So, I use it basically as what I call ambient AI to support my entire life. And we'll go through all the things it's doing, but basically at all times of the day, each one of these computers is just burning tokens, doing things, helping my life, right? Unlimited AI is basically how local AI works. And I all have them doing jobs around the clock for me, which uh you really can't do with cloud uh you know, APIs.

Claire Val: So, how how did we get here? How did you become hardware local model guy? Like like why is it so hot in your in your office? What what brought you to this moment?

Alex Finn: My big awakening was back in January when I discovered OpenClaw. And so I was scrolling X one day on a Friday. I saw a blog post around openclaw. I open it. I'm like, "Wow, this is really interesting." Don't know why. My gut instinct like I got to go buy a Mac Mini. I go to this, this is before anyone was talking about OpenClaw, by the way. I get a Mac Mini, put it on, start using it. It was one of the most aha awakening moments of my life. And something about building this like personal bond with the Open Claw and with this agent was like, I want this to live in the computer. I I don't want this to come from the cloud. I don't want this I want this working for me on my computer. And like this is the future. just having this kind of personal assistant on your computer. And so I started doing research, okay, how can I run uh models locally and I came inclusion, oh, I'm going to go now after buying this Mac Mini and buy a Mac Studio with tons and tons of RAM and run local models and run it locally. And so that was kind of my, I guess, red pill moment into local AI was using Open Cloud. It only just advanced the last couple months, you know, then they start banning Frontier models like Fable and then they, you know, the hardware prices start exploding and it just felt like everything was moving in this direction of like sovereign own intelligence. And so I just investing more and more and more into it over the the last couple months.

Claire Val: So I I mean I had a very similar moment which is I just like got deeply clawilled in in January. Like it just I remember I told this story. I woke up one morning and turned to my husband cuz this is the kind of things I say to him and I was like I'm having a chat like truly having a Chad GBT moment where I think everything from here on out is going to be totally different. So every red pill that we took is that little lobster color I think. And I look I accidentally by accidentally I just like waited too long to get a studio and now literally I can't afford one or find one. But I do have little fat stacks of minis back here that are doing plenty of good work but through through cloud API. So I'm I'm a little bit behind you.

Alex Finn: I'm a strong believer though that those minis will be very useful soon enough. Like Google's doing a lot of research around like optimizing models so they can run on hardware like minis. So you won't be out of the game for long.

Claire Val: Well, and I still I still find use for them. They're still they're still happily at work. They're just at expensive expensive cloud work. But let let's go to you know you you have all these these machines you have bought them and you have built them kind of what's good for what can you walk me through like how you make decisions and how you actually just get these models set up and running on on these machines.

Alex Finn: Yeah, so I have been uh experimenting the last few months around the different machines, the different capabilities, what they're good for, what they're weak for. Obviously I start out with the Mac Studio. bought three of these 512 gigabytes. They'\ve been great. Uh but since then, I've experimented with kind of AI focused computers like the DGX Spark. Uh and then just recently, most recently, I've been buying kind of traditional GPUs from Nvidia like the 5090, soon the 6000 Pro. I put together a little bit a little chart here.

Claire Val: Yeah.

Alex Finn: Uh which I'll show you. So, you basically have four different options. your Mac studios,

Alex Finn: your AI computers like the DGX Spark, which is getting very popular now, your kind of powerhouse traditional Nvidia chips like the 5090, building computers around these chips, and then basically everything else, laptops, Mac minis, whatever you got sitting around your house. Uh, they all have different strengths and weaknesses, different reasons why you'd want to go for one over the other. First, there's the Mac Studio. What's very powerful about Macs and the reason why a lot of people are going for them is they have what's called high unified memory. When you buy like an Nvidia chip for instance and you build it into your computer, you have kind of two separate types of memory. You have your memory that runs all your, you know, programs and then you have your memory which is like your VRAM which is for your graphics. And when you run AI models on that, it's all in the VRAMm. The issue is the VRAM is very small. With the Mac, it's unified, so everything's the same. So whatever memory you have on your computer can be used for graphical processing. And so if you buy a Mac with a ton of memory, 512 GB, 256, even 128, you can use all of that for graphical processing. And that graphical processing is what you know runs the AI models. So, Mac Studios are fantastic for huge big models because you can use unified memory, right? I'm running GLM 5.2, which is Opus 48 level, you know, intelligence on one Mac Studio right now, which is unbelievable. The downside is you have very low memory bandwidth with Mac computers, which means it can't process a lot of it all at once, which means speeds are very slow, right? Right. So the the the models are very very slow. If I send a prompt to GLM52 right now on my Mac Studio, it might take 5 minutes for me to get a response back. So it is very very slow, but you have frontier intelligence uh running on your hardware, which is amazing. Then you have AI computers. This is like the DGX Spark. Uh very popular right now. It's like I think the price just went up to like I think $4,600. Microenter I think you had for $4,000. Um, these are plug-and-play AI workstations. Uh, they have actually unified memory with Nvidia. So, you do get a lot of memory, 128 gigabytes, and you also get pretty decent bandwidth and you get the Nvidia architecture, which is called CUDA, which basically gives you a lot of speed as well. So, it's kind of this sweet spot where you get decent memory and decent speed. And so you can run like kind of these midsize good models like Quen 36 pretty quickly, which is nice. It's also very plug-and-play. These AI computers, you don't even need a monitor. You just plug it into the wall and you can connect to it. And then lastly, you have your traditional Nvidia chips. This is when you go on Twitter and you see the people saying buy GPUs and they have these huge racks of all these chips. These are all Nvidia chips and these are the, you know, probably the most powerful of all the three. Uh, you get kind of lower VRAM, right? The 5090, which is a $4,000 chip, only has 32 gigs of VRAM, but it is lightning fast. It is extremely high bandwidth, and you're getting like cloud speeds, but locally, which is really amazing. So, these are really like your three. Then you have everything else, the Mac minis, your old laptop. You can still run local models on them. There's options out there like Gemma 4, um, like a few other really small ones. They're not going to be anything close to Frontier, but you can do small things like embedding, which is basically managing memory for your agents. So you have these options and so Mac Studio and just like big beefy models, high intelligence, slow kind of these AI computers kind of like sweet middle spot and then these chips very very speedy. Um and then you know industrial computers maybe like these like um point solution models that are like small for a specific use case that might be beneficial to be running in some context but are probably not going to be the thing that you just rely on over and over again. Um though you still on dusty old computers what I say is like you can still put them to work in terms of parallelizing your your cloud work across across computers. So, we have a bunch of old computers and it's like I cannot do enough on my MacBook Pro. Im kicking off stuff to other computers just to like run work trees and do all sorts of interesting things. I'm sure I could do that in the cloud, too, but you know, I like to make use of all this this hardware sitting around.

Claire Val: No, and it's it's uh I like to do that as well. I have two Mac minis and I do the exact same thing. And these apps are so good now. Like Codeex, for instance, is maybe the best agent harness out there right now.

Alex Finn: Yep. You can just go to codeex and be like, "Hey, go on the Mac Mini, right? Uh, start building an app on there, test it yourself, do play, write, so you can go through the flow, screenshot things, send me videos." So, I actually like running it locally like on the Mac Mini as opposed to cloud because you can have it actually click through and do things and then like send you screenshots of what it's doing. So, it's it's a lot better for that, too.

Claire Val: Completely agree. Okay. And then how do you get these, you know, just high level set up with these models? What is kind of your typical install on any of these machines?

Alex Finn: So the good news is, uh, OpenClaw and Hermes has made this process 10 trillion times easier, right? Before you would have to go find the right model, find the right version of it, uh make sure it can fit into memory, download it, run it on a server, all these really complex things that a normal person would never be able to do in like a thousand years. Well, the good news is you can now take Hermes or OpenClaw, basically make it your IT guy, say, "Hey, check out my Mac Studio, check out my DGX Spark, whatever you got, see what the hardware is, and then find whatever model you think's most appropriate for that hardware, and load it up." And then so as long as you have an agent Hermes or OpenClaw as well as tail scale which basically allows you to create a private network across all your devices your agents basically your IT guy and can go across all of your devices install whatever models it needs. Set it up do whatever you need. Uh I'm going to show you a dashboard soon which shows all my models working and running. It's all coordinated and run by my Hermes and my Open Claw. So, as long as you have Hermes OpenClaw going, you're going to say, "Hey, OpenClaw, check out the new Mac Studio I just bought, think look at all the hardware, figure out which model we should run, think about the use cases that are appropriate for me, and then load up a model and get it going." And like, you don't need any technical knowledge whatsoever. They'll go across your devices on tail scale and set it up for you. There really is like no technical work needed.

Claire Val: And so to repeat this for folks just so you so I'm I'm understanding you have a machine set up with openclaw. You also have all your machines on tail scale which allows you to have this like little virtual private network and then that one sort of like IT guy openclaw or Hermes agent can then use tail scale to just go into these different machines configure and manage them.

Alex Finn: Yeah. Exactly. So, Tailscale like is worth getting even if you just have one computer because it creates this private network where even if you're vibe coding like on your MacBook Pro and you just have a phone, you can like go on the local host on your phone and from that's on your computer and like test your local apps out because it's all on the same network now. But it's even better if you have multiple computers. So maybe you have your MacBook Pro, then you buy a Mac Studio for AI or a DGX Spark. You install tail scale on all of them and then you can just say hey go on this other computer and do this load up the model whatever and it will jump between all your devices no technical knowledge needed and load up and run anything you want.

Claire Val: This episode is brought to you by Jira product discovery. AI has made individual PMs incredibly productive but multiplayer mode is where it still breaks. Getting everyone aligned on what should actually get built. Decisions live in a markdown file from last week. The road map's a spreadsheet no one's looking at. Jurro [music] product discovery is where teams actually decide what to build. Capture ideas, prioritize them as a team, and share a living road map everyone works from. It's powered by Atlassian's teamwork graph, so it can pull in customer feedback, what your team shipped, plus your goals, and suggest what to build next. And when a decision is made, you can hand it off straight to Jira so a developer or even an agent can pick it up and start building. Teams at Canva, Deliveroo, and Toast already use Jira product discovery. Join more than 25,000 teams at aten.com/howi. Start building the right things together. Okay, so we're we've we've talked enough about hardware. We've talked enough about how to get it set up. Show me how you use it. So, what are you using all this sort of intelligence for? And how do you keep it burning tokens uh you know, effectively?

Alex Finn: I've been building a system over the last couple months to coordinate all these computers and all these models. I do a lot of different things. Just to kind of set the stage here, the number one push back I get on all this is, well, your computers are so expensive. Isn't cloud models cheap? Isn't it $20 for a Chad GBT subscription? Why the hell would I buy a $10,000 Mac Studio? That's like 11 years of Chad GBT usage. Well, that's not the point. The point is in pure ROI. Not everything in your life is pure ROI, dollars and cents, right? The point is the use cases it unlocks, right? You now have because you have AI models running locally, the ability to run unlimited intelligence around the clock 24/7. If you were to do that with a cloud model like Chad GBT or Claude, you would be spending outrageous amounts of money. So, you wouldn't be running it 24/7 burning tokens around the clock. But because it's local, because you have unlimited usage of it, you can burn tokens around the clock. And so that brings me to my fleet control. I call it this is my fleet dashboard, which allows me to see all my computers, uh, which models are running on them currently and monitor everything they're doing and organize their 247 365 tasks throughout the day. The local models I have running are constantly doing work for me to support my life, to support my many lines of business. I'm building a SAS right now, Henry intelligent machines. Uh, every 30 minutes to an hour, one of these local models does security scan. So, actually picks out an API endpoint or some part of my code, runs a security scan on it, and makes sure it's secure. another local model every half hour or so does a code review, picks out some piece of code and delopifies it, right? Finds ways to optimize it, finds ways to speed it up, finds ways to make it better. Another local model will every 20 minutes look at Twitter, Reddit, Product Hunt, uh Hacker News and look for signal, right? As a problem solver, the only way I can solve problems is if I find them, right? And so I have this ambient AI going online 24 hours a day reading all the social media sites looking for signal, looking for challenges people are having. If someone goes, "Man, I really wish I had a piece of software that allows me to edit my videos like this or something like that." My agent will find that signal, put it in my queue, and I'll be like, "Okay, can I build a SAS to solve this? Can I build a a program to solve this?" And this is there's many screens here I can go through but that is from a high level uh what these agents are doing and the strength of local AI is you can have it running 24/7 365 just doing different things online for you

Claire Val: and so for the let's just talk about the coding use cases really quickly. So for these like automated security scans, automated like quality checks, do you feel like the the local models are of sufficient intelligence to get the job done? And are there specific models that you've applied to in particular the coding use case?

Alex Finn: When getting into local models, you really want to understand what is the delineation between what you want to do with local models and what you want to do with frontier intelligence. Right? You don't want your entire security apparatus to be run by local models. The intelligence just isn't there yet. But where the advantages and the strengths come in is basically it runs this security scan, looks for these challenges and then what it does is every day it builds a report. And you can see some of them here where it'll build a report around what the security issue is, what the code snippet is, uh what the problem is, put it all in this markdown file that describes like for instance today's has 374 findings in it of security issues. Every day I have a loop running in clawed code. So slash loop 24 hours where it goes and takes whatever the latest finding is from the local models and reviews it and then goes through the code exactly where it points to and sees if it's a real issue and how to fix it or not. So it's you know if I were to loop claw code like the local model is doing every 20 minutes look at different I'd be spending thousands thousands of thousands of dollars a month. But the you know the this is almost like the business development rep right that's going qualifying leads and then claude code's like the closer taking those leads okay what can we fix so like you don't have the closer doing all the work

Claire Val: how do you federate work across these machines and so are like do you have some that you're like this is my coding machine this is my market research machine are you like roundroining again we're like using very SDR terms I'm like are we roundrobining these leads like have

Claire Val: we both asked Too long. We both in the SAS world way too long.

Alex Finn: Exactly. Yeah. How do you how do you do that allocation?

Alex Finn: Again, every model computer has different strengths. GLM52 is opus level, right? But it's very very slow. So I don't need it doing super fast work. So I can have it doing the security scans, right? I can have it go and do security scans. Even if it takes 24 hours, that's fine. and claude codes only checking the report once a day. So it can do the kind of super critical smart stuff. On the other end, Quen 36, which is not quite as smart as dealing, I can have that reading Twitter and Reddit all day just looking for signal. That's a very simple thing to find. Look for someone who has a challenge. And so it's just pulling in data, crunching it, and reading for challenges, which you don't need the highest intellect. So you just need to know like what's the strength of the computer, what's the strength of the model and what would be tasks that be appropriate for those strengths.

Claire Val: Is it in this daily brief that you have these tasks assigned to you? Like is this the dashboard for you, the agents and the machines all to collaborate?

Alex Finn: So I'm going to advance it to the point where it's like collaborative with claud code and codeex eventually. Right now the system where claud code is like looping every day and going in that's happening separately. it's just in its own chat inside cloud code where it loops over and over again. But no, this is this is all around my local um models. But I think where the connector is with everything else going on is with my open claw and my Hermes. They're basically the IT guy that runs this as well. This dash for then communicates with cloud code. Hey, can you kick this off? Can you do this? So, it's kind of all glued together by my personal agent.

Claire Val: Okay. I have to ask you a question. Open Claw Hermes. You You run both? You have a favorite? Tell me.

Alex Finn: I use both because much like Clawed Code in Codeex, it feels like there's some days where one is really really dumb and the other is significantly smarter and like I feel like uninstalling one and getting rid of the other. I'll say this though, um the dependability of OpenClaw uh turned me off. Uh there was a run for like a month straight where every update I did broke it and then I had to spend half an hour fixing it. Uh Hermes, I've never had that issue. It seems to be a much more dependable application. I would say this, if both were like 100% dependable, never broke. I can lean on it no matter what. I'd probably be using OpenClaw because I think I've had the most wow impressive big bang moments with OpenClaw. I just can't afford to be like spending a half hour once a week fixing it from breaking for some reason. And so that's I think I've been using Hermes agent a little bit more lately.

Claire Val: That's I mean that was my general takeaway as well is like OpenClaw doesn't doesn't functionally work but it works in my heart and Hermes functionally works but has not cracked my heart yet. The the trick that I have I mean we all have like agents managing agents is now I have the lifeguard. He's an open claw. He runs on his own gateway. And his only job is to keep the other agents alive. He does not get upgrades as much as everybody else because truly I was spending like you all my time trying to figure out why my agents were broken. Um but I just I just love I love my little open claws. I can't I can't get over it. It's just too good.

Alex Finn: I have an emotional attachment to my open claw. I've never had an emotional attachment to my Hermes. Uh but I eventually got to the point where like I don't care about emotions anymore. I just got to get work done. But I have a similar setup where I I have so much failover. So I have uh I think three Hermes agents I'm running uh an Opus one, a Chad GBT1 and on one on a local model and then two open claws, an Opus and a Chad GBT1. And like at any given point of those five agents like three are always down for one reason or other. But the good news is I have two that can go and fix the others. So I I have that failover as well.

Claire Val: Perfect. Well, you showed us so much in terms of like how to pick hardware, how to set up hardware, how to manage all this compute across all your machines, use cases from engineering perspective, from a market discovery perspective. What else fun are you doing with AI? any other ones where you're like, I didn't get to show a fun workflow, but just something that's really been a delight for you to use with either local or kind of cloud models.

Alex Finn: The most fun I've been having lately is building out my uh software factory. So, there's been this big trend the last month uh and I I'll show this to you as well in a second of uh people talking about loops online. And everyone's kind of like vague posting about loops. Uh, which is, you know, at first it was kind of angry me. It was pissing me off a little bit. Like, why are these people vague posting about loops? Why why wouldn't they go into it? So, I spent like days locked in trying to figure out how I can make a really productive loop. And I've just had like a blast the last few weeks trying to make this loop system completely autonomous. And this is like part of what I showed you ties into it. like the security reviews, the code reviews ties into this loop. Um, but basically what I have doing, I'll try to show you enough here. Let's see what I can show you. Basically, what I have doing is I have uh two loops in cloud code going. Uh, I have a build loop and a review loop. And basically what I do is first I'll start I'll show you cloud code in a second. First, what I do is I go into Claude in the morning and I go uh morning build and it asks me a bunch of questions what I'm thinking about. Comes up with a bunch of tasks uh to build for my SAS uh Henry intelligent machines. And then what it does is it goes in here and uh first it has a build loop where it'll take all those tasks and start building out the tasks it came with over and over and over again. And then it has a review loop that takes all the tasks that were built and has another agent go in and review it and fix any code or anything like that. And then from there, once that's reviewed, I can go in to Slack. It pings me on Slack and it shows me everything that was built and reviewed and I can just leave a rocket emoji. And when I leave the rocket emoji, it says merged and my Henry loop goes and merges it. And so I have this new workflow. I've I've had a blast building out, which I think is the future of like software development where, you know, before you you you you uh prompt your agent all day, hey, build this. Now, build this. Now, build this. Now, build this. And you kind of handhold it as it builds things. Now, it's I go in, we talk to each other a little bit, comes up with a bunch of ideas, spends all day building it and reviewing itself and testing it out. At the end of the day, I come in, I see what's merge ready, and I just leave a rocket ship emoji on every single thing that looks ready. shows me exactly how to test it. Puts it on its own versel preview uh site and I can go in test it out and then give it a rocket ship emoji. So, this has been like the most fun interesting thing I've been doing recently and you know I'm I'm figuring out how the local models can tie into it and support this. But it's been a blast kind of cracking this nut of how do you just like build your own software factory?

Claire Val: I love it. I love it so much. It's given me some inspiration on some things that that I'm going to do with my own loops. And in case folks missed it, I did do a WTF our loops episode about a week ago. Very clear. I do three loops that you can copy and paste. So if you are still trying to figure out I too was like why are people being vague about loops? Like this is not a scary or mysterious thing. So we just popped up the screen share and and showed a couple of them.

Alex Finn: My thesis by the way I'll share the thesis real quick. My thesis that why everyone's being vague and not like sharing how they're doing it like neither open AI or CL like they talk about a lot but they haven't shared it is like I think this is kind of the last moat for a lot of these companies is like you know anyone could build anything they want but the actual infrastructure around it and like how you automate that to put out more code like they probably have their own systems that pump out a ridiculous amount of high quality code. if they were to share how they do it, other people be able to copy and they'd be able to be equally as productive. But like this is kind of a motive theirs now. Like if they can figure out a really good system that builds high quality codes. I think that's why they're being vague and not sharing it.

Claire Val: I and I have a I have a suspicion about why non kind of model people are being vague, which is one, their use of loops is probably pretty boring.

Claire Val: They're not like, "Guess what? This is my loop. in the morning it runs acron and does this thing. Um and two they probably get more eyes being vague than being specific. So I am very cynical about about the vague posting which is why we are a screen sharing um podcast here. Well, Alex, this has been super fun. Let's do quick lightning round and then I will get you out of here. Question number one. Of all the hardware, which one is your favorite?

Alex Finn: I'm torn between the Mac Studio and the 5090. I like the Max 2 because I love the uh integration with all Apple device. Everything owns Apple, iPhone, iPad. The integration, being able to run models side by side locally, first of all, I think is the future of computing. I think Apple will start running local models built in that help you out within the next 10 years. But I feel like I kind of lean the 5090 because I can play Cyberpunk 20207 uh 2077 in all ultra with all the hyperrealistic mods. So, I think I got the line uh lean the 5090.

Claire Val: Okay. And then on the model side of all the the models that you have locally installed, do you have one that you just you just really love?

Alex Finn: I've So, I was on Quen 3 uh first 35, then 36 for basically the entire span of last five months I've been on local models, but a new model just came out. I know nothing about the team. And I've done zero research, so I hope to god I'm not promoting bad people, but it's called Ornith 1.0, and I think they did some like uh reinforcement learning on Quen and improved it and made it even better at coding. And every eval I've run on it has shown that it's better than Quen. It's faster and smarter. And so, Ornith 1.0 35B has been uh my most used model recently. And you can run on a DJX Spark. So, anyone who has that, you can load it up and it works great. Who thought that you and I SAS people would just be like Ornith 1.0 83B DGX SP like just letter after letter after letter. But this is our life now. I love it.

Claire Val: We both had the same background working for MA SAS marketing tools uh spamming people all day with email to now uh the nerdiest technology on planet Earth.

Alex Finn: That's exactly right. And then last question I ask everybody, when your open claw, your Hermes agent is being real dumb and not listening to you, what is your prompting strategy? Are you extremely polite or less so?

Alex Finn: Let's just say this. If my chat logs were to ever leak to the internet, first of all, you would never have me on your podcast again. Uh second of all, I think I'd be taken off every single social media site. So, I am uh a pretty nasty person to my agents. Uh I am not nice to them. I've I'll just say I've threatened them multiple times. The threats never seem to work. Even though I threatened to hurt their agent family, doesn't work. They still fail. But uh uh yeah, I'm pretty mean, but I find that um when just much like Claude Code and Codex, when one's stupid, I just go to the other until things calm down and they figure out what's going on and it gets fixed and then eventually they're smart again.

Claire Val: Amazing. I love it. Well, Alex, this has been so fun. Where can we find you and how can we be helpful?

Alex Finn: I'm Alex Spin on YouTube. Alex Spin on X. Uh I uh have a uh community. You can join the Vibe Code Academy. I also have two SAS. I'm working on Creator Buddy, which is a basically operating system for [clears throat] Twitter, as well as Henry intelligent machines, which is coming soon. So, if you just subscribe to me on YouTube, you'll hear about all the other things eventually.

Claire Val: Amazing, Alex. Well, I will get you back to your machines and back to building. Thanks for joining How AI.

Alex Finn: Thanks so much for having me, Claire. Thanks so much for watching. If you enjoyed this show, please like and subscribe here on YouTube, or even better, leave us a comment with your thoughts. You can also find this podcast on Apple Podcasts, Spotify, or your favorite podcast app. Please consider leaving us a rating and review, which will help others find the show. You can see all our episodes and learn more about the show at howiipod.com. [music] See you next time.